AI Threat Alert
Survey HIGH
Paraphrasing Adversarial Attack on LLM-as-a-Reviewer
Masahiro Kaneko
The use of large language models (LLMs) in peer review systems has attracted growing attention, making it essential to examine their potential...
AI Security Research
AI Threat Alert indexes 3,771+ peer-reviewed and preprint papers on AI/ML security — covering adversarial attacks, model defenses, red-teaming benchmarks, surveys, and security tooling. Papers are sourced from arXiv, classified by type and by relevance to real-world threats, and cross-referenced with the CVEs and incidents they relate to.
- Adversarial attacks
- Model defenses
- Red-teaming benchmarks
- Surveys
- Security tooling
- Total3,771
- Attack1,458
- Benchmark1,072
- Defense533
- Tool380
- Survey220
Showing 2581–2600 of 3,771 papers
Survey MEDIUM
CHASE: LLM Agents for Dissecting Malicious PyPI Packages
Takaaki Toda, Tatsuya Mori
Modern software package registries like PyPI have become critical infrastructure for software development, but are increasingly exploited by threat...
Benchmark MEDIUM
CyberLLM-FINDS 2025: Instruction-Tuned Fine-tuning of Domain-Specific LLMs with Retrieval-Augmented Generation and Graph Integration for MITRE Evaluation
Vasanth Iyer, Leonardo Bobadilla, S. S. Iyengar
Large Language Models (LLMs) such as Gemma-2B have shown strong performance in various natural language processing tasks. However, general-purpose...
Attack HIGH
ALFA: A Safe-by-Design Approach to Mitigate Quishing Attacks Launched via Fancy QR Codes
Muhammad Wahid Akram, Keshav Sood, Muneeb Ul Hassan +1 more
Phishing with Quick Response (QR) codes is termed as Quishing. The attackers exploit this method to manipulate individuals into revealing their...
Survey LOW
AI Hallucination from Students' Perspective: A Thematic Analysis
Abdulhadi Shoufan, Ahmad-Azmi-Abdelhamid Esmaeil
As students increasingly rely on large language models, hallucinations pose a growing threat to learning. To mitigate this, AI literacy must expand...
Defense LOW
SafePro: Evaluating the Safety of Professional-Level AI Agents
Kaiwen Zhou, Shreedhar Jangam, Ashwin Nagarajan +7 more
Large language model-based agents are rapidly evolving from simple conversational assistants into autonomous systems capable of performing complex,...
Attack HIGH
Leveraging Soft Prompts for Privacy Attacks in Federated Prompt Tuning
Quan Minh Nguyen, Min-Seon Kim, Hoang M. Ngo +3 more
Membership inference attack (MIA) poses a significant privacy threat in federated learning (FL) as it allows adversaries to determine whether a...
Benchmark MEDIUM
Burn-After-Use for Preventing Data Leakage through a Secure Multi-Tenant Architecture in Enterprise LLM
Qiang Zhang, Elena Emma Wang, Jiaming Li +1 more
This study presents a Secure Multi-Tenant Architecture (SMTA) combined with a novel concept Burn-After-Use (BAU) mechanism for enterprise LLM...
Tool LOW
Cross-Border Data Security and Privacy Risks in Large Language Models and IoT Systems
Chalitha Handapangoda
The reliance of Large Language Models and Internet of Things systems on massive, globally distributed data flows creates systemic security and...
Attack HIGH
Are LLMs Vulnerable to Preference-Undermining Attacks (PUA)? A Factorial Analysis Methodology for Diagnosing the Trade-off between Preference Alignment and Real-World Validity
Hongjun An, Yiliang Song, Jiangan Chen +3 more
Large Language Model (LLM) training often optimizes for preference alignment, rewarding outputs that are perceived as helpful and...
Defense MEDIUM
SecureDyn-FL: A Robust Privacy-Preserving Federated Learning Framework for Intrusion Detection in IoT Networks
Imtiaz Ali Soomro, Hamood Ur Rehman, S. Jawad Hussain ID +3 more
The rapid proliferation of Internet of Things (IoT) devices across domains such as smart homes, industrial control systems, and healthcare networks...
Attack MEDIUM
On the Adversarial Robustness of 3D Large Vision-Language Models
Chao Liu, Ngai-Man Cheung
3D Vision-Language Models (VLMs), such as PointLLM and GPT4Point, have shown strong reasoning and generalization abilities in 3D understanding tasks....
Benchmark MEDIUM
VIPER Strike: Defeating Visual Reasoning CAPTCHAs via Structured Vision-Language Inference
Minfeng Qi, Dongyang He, Qin Wang +1 more
Visual Reasoning CAPTCHAs (VRCs) combine visual scenes with natural-language queries that demand compositional inference over objects, attributes,...
Benchmark MEDIUM
Lightweight Yet Secure: Secure Scripting Language Generation via Lightweight LLMs
Keyang Zhang, Zeyu Chen, Xuan Feng +4 more
The security of scripting languages such as PowerShell is critical given their powerful automation and administration capabilities, often exercised...
Benchmark MEDIUM
Why LoRA Fails to Forget: Regularized Low-Rank Adaptation Against Backdoors in Language Models
Hoang-Chau Luong, Lingwei Chen
Low-Rank Adaptation (LoRA) is widely used for parameter-efficient fine-tuning of large language models, but it is notably ineffective at removing...
Benchmark MEDIUM
Agentic LLMs as Powerful Deanonymizers: Re-identification of Participants in the Anthropic Interviewer Dataset
Tianshi Li
On December 4, 2025, Anthropic released Anthropic Interviewer, an AI tool for running qualitative interviews at scale, along with a public dataset of...
Attack HIGH
Cybersecurity AI: A Game-Theoretic AI for Guiding Attack and Defense
Víctor Mayoral-Vilches, María Sanz-Gómez, Francesco Balassone +6 more
AI-driven penetration testing now executes thousands of actions per hour but still lacks the strategic intuition humans apply in competitive...
Defense MEDIUM
StriderSPD: Structure-Guided Joint Representation Learning for Binary Security Patch Detection
Qingyuan Li, Chenchen Yu, Chuanyi Li +4 more
Vulnerabilities severely threaten software systems, making the timely application of security patches crucial for mitigating attacks. However,...
Tool HIGH
VIGIL: Defending LLM Agents Against Tool Stream Injection via Verify-Before-Commit
Junda Lin, Zhaomeng Zhou, Zhi Zheng +4 more
LLM agents operating in open environments face escalating risks from indirect prompt injection, particularly within the tool stream where manipulated...
Attack HIGH
The Echo Chamber Multi-Turn LLM Jailbreak
Ahmad Alobaid, Martí Jordà Roca, Carlos Castillo +1 more
The availability of Large Language Models (LLMs) has led to a new generation of powerful chatbots that can be developed at relatively low cost. As...
Frequently asked questions
What is AI security research?
AI security research studies how AI and machine-learning systems can be attacked and defended — covering adversarial examples, prompt injection, model poisoning, training-data extraction, and the mitigations against them. AI Threat Alert curates this research from academic sources so security teams can track the threats behind emerging AI risks.
How many AI security papers does AI Threat Alert track?
AI Threat Alert indexes 3,771+ papers on AI/ML security, classified across attack, defense, benchmark, survey, and tool categories and updated continuously.
Where do the research papers come from?
Papers are sourced from arXiv, then classified by type and by relevance to real-world AI/ML threats, and cross-referenced with the CVEs and incidents they relate to.
What topics does the AI security research cover?
Coverage spans adversarial attacks, model and system defenses, red-teaming benchmarks, literature surveys, and security tooling for LLMs, ML libraries, AI agents, and inference pipelines.
How is this different from a generic paper search?
Every paper is filtered for AI security relevance and linked to the vulnerabilities, vendors, and incidents it relates to, so the research connects directly to operational threat intelligence.
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